Query Scheduling via Resource Allocation and Availability
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Solution Overview
Problem
Existing data intake and query systems struggle to seamlessly search and analyze large sets of diverse data from both internal and external data sources, lacking tools for quick and easy visualization of data subsets, and are limited in scope to their internal data stores, making it difficult to derive comprehensive insights from raw data across various data systems.
Innovation Solution
A data intake and query system that extends search and analytics capabilities by employing a search process master and query coordinators, coupled with a scalable network of distributed nodes, enabling processing and analysis of data across diverse data systems, including external data sources such as MySQL, PostgreSQL, Oracle databases, NoSQL data stores, and cloud storage, and providing integrated visualization tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a data intake and query system stores and processes large amounts of diverse raw data from multiple sources, then the ability to derive comprehensive insights and flexibility of analysis is improved, but the system complexity and resource requirements increase significantly
Solution Approach 1:
The system divides the data intake and query functionality into separate modular components: data intake modules that ingest data from various sources, query modules that process search requests, and analysis modules that derive insights. This segmentation allows each component to be optimized independently while maintaining overall system flexibility.
Solution Approach 2:
The patent implements universal data structures and processing pipelines that can handle multiple data types (structured, semi-structured, unstructured) from diverse sources (databases, cloud storage, file systems). The query system provides multi-functional capabilities to search, analyze, and visualize data across different formats and sources through a unified interface.
2Adaptability or versatility
If the system extends search capabilities to external data sources beyond internal data stores, then the comprehensiveness of data analysis is improved, but the difficulty of integrating and managing diverse data sources increases
Solution Approach 1:
The patent introduces intermediary components including data connectors and adapters that mediate between external data sources and the core query system. These intermediaries standardize data access protocols, handle source-specific authentication and formatting, and present a unified interface to the query engine, thereby simplifying integration of diverse external sources.
Solution Approach 2:
The system dynamically adjusts query parameters and data access configurations based on the specific external data source being accessed. Different data sources (MySQL, PostgreSQL, Oracle, NoSQL, cloud storage) have their specific parameters and connection settings automatically configured and optimized, allowing comprehensive data access without manual integration complexity.
3Measurement precision
If the system processes and analyzes massive quantities of raw data, then the quality and depth of insights derived is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary data processing and indexing operations during data ingestion. Data is pre-processed, validated, and indexed as it enters the system, organizing it into optimized data structures that enable faster query execution. This preliminary action reduces the computational burden during actual analysis operations.
Solution Approach 2:
The system maintains continuous data processing pipelines that ingest, index, and make data searchable in near-real-time as data arrives from various sources. This continuous processing ensures that data is always in an optimized state for analysis without requiring batch processing interruptions, thereby reducing overall processing time while maintaining insight quality.
Data Source
AI summary
Systems and methods are described for scheduling a query for execution. The system receives and parses a query to identify one or more portions of the query. The system determines a resource allocation for each portion of the query, and determines an availability of compute resources for the different portions of the query. Based on the resource allocation and the availability of compute resources, the system schedules the query.